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Sending OpenAI Chat Completion Traces

This guide explains how to send OpenTelemetry-compatible traces for OpenAI chat completions to Langtrace using cURL. Here’s an example of how your traces will appear in the Langtrace UI after following this guide: OpenAI Chat Completion Trace Example

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Prerequisites

  • Langtrace API Key
  • OpenAI API Key (for comparison with SDK usage)

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Endpoint

POST http://localhost:3000/api/trace  # For local development
# or
POST https://api.langtrace.ai/api/trace  # For production

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Headers

Content-Type: application/json
x-api-key: YOUR_LANGTRACE_API_KEY
User-Agent: opentelemetry-python

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Trace Format

The trace must follow the OpenTelemetry format and include specific attributes for OpenAI chat completions. Note that attributes must use the OpenTelemetry value format with stringValue or intValue: 
{
  "resourceSpans": [{
    "resource": {
      "attributes": [{
        "key": "service.name",
        "value": { "stringValue": "your-service-name" }
      }]
    },
    "scopeSpans": [{
      "scope": {
        "name": "openai",
        "version": "1.0.0"
      },
      "spans": [{
        "name": "OpenAI ChatCompletion",
        "kind": 2,  // SpanKind.CLIENT
        "startTimeUnixNano": "1234567890000000000",
        "endTimeUnixNano": "1234567891000000000",
        "traceId": "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6",
        "spanId": "a1b2c3d4e5f6g7h8",
        "attributes": [{
          "key": "llm.vendor",
          "value": { "stringValue": "openai" }
        }, {
          "key": "llm.request.model",
          "value": { "stringValue": "gpt-3.5-turbo" }
        }, {
          "key": "llm.request.messages",
          "value": { "stringValue": "[{\"role\": \"user\", \"content\": \"Hello\"}]" }
        }, {
          "key": "llm.path",
          "value": { "stringValue": "/v1/chat/completions" }
        }, {
          "key": "llm.request.temperature",
          "value": { "intValue": 0.7 }
        }, {
          "key": "llm.request.max_tokens",
          "value": { "intValue": 150 }
        }, {
          "key": "llm.usage.prompt_tokens",
          "value": { "intValue": 10 }
        }, {
          "key": "llm.usage.completion_tokens",
          "value": { "intValue": 15 }
        }, {
          "key": "llm.usage.total_tokens",
          "value": { "intValue": 25 }
        }]
      }]
    }]
  }]
}

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Example cURL Command

curl -X POST "http://localhost:3000/api/trace" \
  -H "Content-Type: application/json" \
  -H "x-api-key: ${LANGTRACE_API_KEY}" \
  -H "User-Agent: opentelemetry-python" \
  -d '{
    "resourceSpans": [{
      "resource": {
        "attributes": [{
          "key": "service.name",
          "value": { "stringValue": "openai-test" }
        }]
      },
      "scopeSpans": [{
        "scope": {
          "name": "openai",
          "version": "1.0.0"
        },
        "spans": [{
          "name": "OpenAI ChatCompletion",
          "kind": 2,
          "startTimeUnixNano": "'$(date +%s%N)'",
          "endTimeUnixNano": "'$(date +%s%N)'",
          "traceId": "'$(openssl rand -hex 16)'",
          "spanId": "'$(openssl rand -hex 8)'",
          "attributes": [{
            "key": "llm.vendor",
            "value": { "stringValue": "openai" }
          }, {
            "key": "llm.request.model",
            "value": { "stringValue": "gpt-3.5-turbo" }
          }, {
            "key": "llm.request.messages",
            "value": { "stringValue": "[{\"role\": \"user\", \"content\": \"Hello\"}]" }
          }, {
            "key": "llm.path",
            "value": { "stringValue": "/v1/chat/completions" }
          }, {
            "key": "llm.request.temperature",
            "value": { "intValue": 0.7 }
          }, {
            "key": "llm.request.max_tokens",
            "value": { "intValue": 150 }
          }, {
            "key": "llm.usage.prompt_tokens",
            "value": { "intValue": 10 }
          }, {
            "key": "llm.usage.completion_tokens",
            "value": { "intValue": 15 }
          }, {
            "key": "llm.usage.total_tokens",
            "value": { "intValue": 25 }
          }]
        }]
      }]
    }]
  }'

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Required Attributes

AttributeDescriptionRequired
llm.vendorService provider (“openai”)Yes
llm.request.modelModel name (e.g., “gpt-3.5-turbo”)Yes
llm.request.messagesJSON string of messages arrayYes
llm.pathAPI endpoint pathYes
llm.request.temperatureTemperature settingNo
llm.request.max_tokensMaximum tokensNo
llm.usage.*Token usage statisticsNo

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Function Calling Example

When using OpenAI function calling, include the tools in the attributes with proper value formatting: 
curl -X POST "http://localhost:3000/api/trace" \
  -H "Content-Type: application/json" \
  -H "x-api-key: ${LANGTRACE_API_KEY}" \
  -H "User-Agent: opentelemetry-python" \
  -d '{
    "resourceSpans": [{
      "resource": {
        "attributes": [{
          "key": "service.name",
          "value": { "stringValue": "openai-test" }
        }]
      },
      "scopeSpans": [{
        "scope": {
          "name": "openai",
          "version": "1.0.0"
        },
        "spans": [{
          "name": "OpenAI ChatCompletion",
          "kind": 2,
          "startTimeUnixNano": "'$(date +%s%N)'",
          "endTimeUnixNano": "'$(date +%s%N)'",
          "traceId": "'$(openssl rand -hex 16)'",
          "spanId": "'$(openssl rand -hex 8)'",
          "attributes": [{
            "key": "llm.vendor",
            "value": { "stringValue": "openai" }
          }, {
            "key": "llm.request.model",
            "value": { "stringValue": "gpt-3.5-turbo" }
          }, {
            "key": "llm.request.messages",
            "value": { "stringValue": "[{\"role\": \"user\", \"content\": \"What's the weather?\"}]" }
          }, {
            "key": "llm.path",
            "value": { "stringValue": "/v1/chat/completions" }
          }, {
            "key": "llm.tools",
            "value": { "stringValue": "[{\"type\": \"function\", \"function\": {\"name\": \"get_weather\", \"arguments\": \"{\\\"location\\\": \\\"London\\\"}\"}}]" }
          }]
        }]
      }]
    }]
  }'

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Response Format

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Success Response

{
  "message": "Traces added successfully"
}

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Error Response

{
  "name": "Error name",
  "message": "Error message",
  "stack": "Error stack trace",
  "fullError": "Detailed error information"
}

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Comparison with SDK Usage

For comparison, here’s how the same trace would be generated using the Python SDK: 
from openai import OpenAI
from langtrace_python_sdk import langtrace

# Initialize clients
client = OpenAI()
langtrace.init(api_key="YOUR_LANGTRACE_API_KEY")

# Make API call - traces are automatically sent
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Hello"}],
    temperature=0.7,
    max_tokens=150
)

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Best Practices

  1. Always include required attributes (vendor, model, messages, path)
  2. Generate unique trace and span IDs for each request
  3. Use accurate timestamps for start and end times
  4. Include usage statistics when available
  5. Format message arrays and tool calls as proper JSON strings
  6. Use the OpenTelemetry user agent to ensure proper trace processing
  7. Include resource attributes with service information
  8. Use proper OpenTelemetry value format (stringValue/intValue) for attributes